The LF-based benchmarking solutions used for the face recognition task include LFLBP [1]}, LFHG [2]}, VGG-D3 [3]}, ResNet50+ST-LSTM [4]}, VGG+Conv-LSTM [5]}, VGG+GLF-LSTM [6]}, VGG+SLF-LSTM [6]}, and ResNet50+DS-LSTM [8]} that have been introduced in Section III. Three non-LF solutions including the VGG-16 [9]}, Resnet50 [10]}, SE-ResNet-50 [10]}, and ArcFace with ResNet-101 backbone [12]} networks, have also been used in the comparisons. Naturally, these non-LF solutions only process the 2D central SA image. It must be noted that some of LF-based face recognition solutions, e.g., [13]}, [14]}, [15]}, consider the a posteriori refocusing capability of LF cameras to improve the image quality of the already captured face images for 2D face recognition. Naturally, these methods are not comparable with the proposed solution which deals directly with the LF data, in its native 4D multi-view data format, to exploit the available spatio-angular information.
